Fetching the paper…
Reading the bibliography…
The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps.
J. A. Cadzow, “Algorithm for the minimum-effort problem,” Automatic Control, IEEE Transactions on
1971
Earlier work this paper cites.
L. Popov, “A modification of the arrow-hurwicz method for search of saddle points,” Mathematical notes of the Academy of Sciences of the USSR
1980
Earlier work this paper cites.
L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica. D
1992
Earlier work this paper cites.
Princeton University Press, Dec. 1996
R. T. Rockafellar, Convex Analysis (Princeton Landmarks in Mathematics and Physics) · 1996
Earlier work this paper cites.
B. He, H. Yang, and S. Wang, “Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities,” Journal of Optimization Theory and Applications
2000
Earlier work this paper cites.
Springer, 2003
A. Schrijver, Combinatorial Optimization: Polyhedra and Efficiency · 2003
Earlier work this paper cites.
T. Chan and S. Esedoglu, “Aspects of total variation regularized ℓ 1 \ell_{1} function approximation,” SIAM J. Appl. Math
2005
Earlier work this paper cites.
E. J. Candes and J. Romberg, “Signal recovery from random projections,” Proc. of SPIE Computational Imaging III
2005
Earlier work this paper cites.
S. H. Han and J. H. Lee, “An overview of peak-to-average power ratio reduction techniques for multicarrier transmission,” Wireless Communications, IEEE
2005
Earlier work this paper cites.
T. Chan, S. Esedoglu, and M. Nikolova, “Algorithms for finding global minimizers of image segmentation and denoising models,” SIAM Journal on Applied Mathematics
2006
Earlier work this paper cites.
E. J. Candes, J. Romberg, and T.Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inform. Theory
2006
Cited alongside, same era.
M. Zhu and T. Chan, “An efficient primal-dual hybrid gradient algorithm for total variation image restoration,” UCLA CAM technical report, 08-34
2008
Cited alongside, same era.
M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, K. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling: Building simpler, smaller, and less-expensive digital cameras,” Signal Processing Magazine, IEEE
2008
Cited alongside, same era.
J. Duchi, S. S. Shwartz, Y. Singer, and T. Chandra, “Efficient projections onto the ℓ 1 \ell_{1} -ball for learning in high dimensions,” in Proc. of the 25th international conference on Machine learning
2008
Cited alongside, same era.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends in Machine Learning
2010
Later among the works it cites.
E. Bae, J. Yuan, and X. C. Tai, “Global minimization for continuous multiphase partitioning problems using a dual approach,” International journal of computer vision
2011
Later among the works it cites.
T. Pock and A. Chambolle, “Diagonal preconditioning for first order primal-dual algorithms in convex optimization,” in Computer Vision (ICCV), 2011 IEEE International Conference on
2011
Later among the works it cites.
H. Bauschke and P. Combettes, Convex Analysis and Monotone Operator Theory in Hilbert Spaces · 2011
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Esser, X. Zhang, and T. Chan, “A general framework for a class of first order primal-dual algorithms for TV minimization,” UCLA CAM Report 09-67
2009
Cited alongside, same era.
T. Pock, D. Cremers, H. Bischof, and A. Chambolle, “An algorithm for minimizing the mumford-shah functional,” in Computer Vision, 2009 IEEE 12th International Conference on
2009
Cited alongside, same era.
A. Chambolle and T. Pock, “A first-order primal-dual algorithm for convex problems with applications to imaging,” Convergence
2010
Cited alongside, same era.
T. Goldstein, X. Bresson, and S. Osher, “Geometric applications of the Split Bregman method: Segmentation and surface reconstruction,” J. Sci. Comput
2010
Cited alongside, same era.
B. Goldluecke and D. Cremers, “Convex Relaxation for Multilabel Problems with Product Label Spaces,” in Computer Vision – ECCV 2010
2010
Cited alongside, same era.
B. He and X. Yuan, “Convergence analysis of primal-dual algorithms for a saddle-point problem: From contraction perspective,” SIAM J. Img. Sci
2012
Later among the works it cites.
T. Goldstein, X. Bresson, and S. Osher, “Global minimization of markov random fields with applications to optical flow,” Inverse Problems in Imaging
2012
Later among the works it cites.
E. Brown, T. Chan, and X. Bresson, “Completely convex formulation of the chan-vese image segmentation model,” International Journal of Computer Vision
2012
Later among the works it cites.
C. Studer, W. Yin, and R. G. Baraniuk, “Signal representations with minimum l ∞ l_{\infty} -norm,” in Proc. 50th Annual Allerton Conference on Communication, Control, and Computing
2012
Later among the works it cites.
L. Condat, “A primal-dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms,” Journal of Optimization Theory and Applications
2013
Closest in time.